Project Summary/Abstract The incidence and progression of chronic or acute infections reflect evolutionary processes, in which pathogens engage in an arms race with their human hosts. While vaccines and antibiotics have enabled mankind to skew the outcome of this contest, these bulwarks against contagion are being eroded. Mutation and natural selection, coupled with rapid generation times and immense population sizes, appear to give pathogens a decisive edge in the evolutionary contest. To regain the upper hand, we must devise new therapeutic strategies that better take into account how our weapons work and how pathogens adaptively evolve to subvert them. Fungal infections impose an enormous disease burden: superficial fungal infections affect as much as 25% of the human population, while each year >2,000,000 invasive fungal infections occur worldwide. Mortality rates for invasive fungal infections are high (20-95%), and their incidence and economic cost are increasing. Of particular concern is the fact that resistance to antifungal drugs is on the rise, with clinical isolates increasingly showing multidrug resistance. To rationally improve upon sequential and combination drug therapies, it is crucial that we understand the ways in which resistance to antifungal drugs arises, and the degree to which resistance to one drug may increase or decrease susceptibility to another. We propose three Specific Aims to capture the ?resistome? of the human fungal pathogen, Candida glabrata. First, we will introduce a modified version of our previously developed DNA-barcode based lineage tracking system into C. glabrata, then experimentally evolve barcoded C. glabrata populations in the presence of four different antifungal agents representing all four major antifungal drug classes. Second, we will isolate hundreds of adaptive lineages from each of these evolution experiments then remeasure their fitnesses in the presence and absence of each of these antifungal drugs. This will enable us to generate a finely resolved picture of the trade-offs and cross-resistance conferred by mutations that arise under selection for resistance to a particular drug. Finally, we will whole genome sequence more than a thousand of these drug-resistant mutants, and measure for each the minimum inhibitory concentration (MIC) for each drug, generating a high-resolution map that describes the relationship between genotype, fitness and drug susceptibility. Completion of these Aims will result in the most comprehensive collection of antifungal resistance mutants ever generated, for which we will know the identities of the mutations and their fitness consequences in the presence of multiple drugs. Our data will provide unprecedented insight into the mechanisms by which resistance arises to all classes of antifungal drugs, which can be used to improve therapeutic strategies, based on the observed patterns of cross-resistance and trade-offs. Our collection of barcoded mutants will open the door to investigation of drug resistance under multi-drug and sequential drug selections and serve as a tool to screen small molecule libraries for the purpose of drug discovery.